In [1]:
# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask
In [2]:
# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')
In [3]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")
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nltk.download('all')

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[nltk_data]    |   Package vader_lexicon is already up-to-date!
[nltk_data]    | Downloading package porter_test to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package porter_test is already up-to-date!
[nltk_data]    | Downloading package wmt15_eval to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package wmt15_eval is already up-to-date!
[nltk_data]    | Downloading package mwa_ppdb to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package mwa_ppdb is already up-to-date!
[nltk_data]    | 
[nltk_data]  Done downloading collection all
Out[4]:
True
In [5]:
# path = '/content/drive/MyDrive/Files/'

path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
 
df_movies = pd.read_csv(path + 'ottmovies.csv')
 
df_movies.head()
Out[5]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country Language Plotline Runtime Kind Seasons Netflix Hulu Prime Video Disney+ Type
0 1 Inception 2010 13+ 8.8 87% Christopher Nolan Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French Dom Cobb is a skilled thief, the absolute best... 148.0 movie NaN 1 0 0 0 0
1 2 The Matrix 1999 16+ 8.7 88% Lana Wachowski,Lilly Wachowski Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... Action,Sci-Fi United States English Thomas A. Anderson is a man living two lives. ... 136.0 movie NaN 1 0 0 0 0
2 3 Avengers: Infinity War 2018 13+ 8.4 85% Anthony Russo,Joe Russo Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... Action,Adventure,Sci-Fi United States English As the Avengers and their allies have continue... 149.0 movie NaN 1 0 0 0 0
3 4 Back to the Future 1985 7+ 8.5 96% Robert Zemeckis Michael J. Fox,Christopher Lloyd,Lea Thompson,... Adventure,Comedy,Sci-Fi United States English Marty McFly, a typical American teenager of th... 116.0 movie NaN 1 0 0 0 0
4 5 The Good, the Bad and the Ugly 1966 16+ 8.8 97% Sergio Leone Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... Western Italy,Spain,West Germany,United States Italian Blondie (The Good) (Clint Eastwood) is a profe... 161.0 movie NaN 1 0 1 0 0
In [6]:
# profile = ProfileReport(df_movies)
# profile
In [7]:
def data_investigate(df):
    print('No of Rows : ', df.shape[0])
    print('No of Coloums : ', df.shape[1])
    print('**'*25)
    print('Colums Names : \n', df.columns)
    print('**'*25)
    print('Datatype of Columns : \n', df.dtypes)
    print('**'*25)
    print('Missing Values : ')
    c = df.isnull().sum()
    c = c[c > 0]
    print(c)
    print('**'*25)
    print('Missing vaules %age wise :\n')
    print((100*(df.isnull().sum()/len(df.index))))
    print('**'*25)
    print('Pictorial Representation : ')
    plt.figure(figsize = (10, 10))
    sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
    plt.show()
In [8]:
data_investigate(df_movies)

No of Rows :  16923
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb               float64
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime            float64
Kind                object
Seasons            float64
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
dtype: object
**************************************************
Missing Values : 
Age                 8457
IMDb                 328
Rotten Tomatoes    10437
Directors            357
Cast                 648
Genres               234
Country              303
Language             437
Plotline            4958
Runtime              382
Seasons            16923
dtype: int64
**************************************************
Missing vaules %age wise :

ID                   0.000000
Title                0.000000
Year                 0.000000
Age                 49.973409
IMDb                 1.938191
Rotten Tomatoes     61.673462
Directors            2.109555
Cast                 3.829108
Genres               1.382734
Country              1.790463
Language             2.582284
Plotline            29.297406
Runtime              2.257283
Kind                 0.000000
Seasons            100.000000
Netflix              0.000000
Hulu                 0.000000
Prime Video          0.000000
Disney+              0.000000
Type                 0.000000
dtype: float64
**************************************************
Pictorial Representation :
In [9]:
# ID
# df_movies = df_movies.drop(['ID'], axis = 1)
 
# Age
df_movies.loc[df_movies['Age'].isnull() & df_movies['Disney+'] == 1, "Age"] = '13'
# df_movies.fillna({'Age' : 18}, inplace = True)
df_movies.fillna({'Age' : 'NR'}, inplace = True)
df_movies['Age'].replace({'all': '0'}, inplace = True)
df_movies['Age'].replace({'7+': '7'}, inplace = True)
df_movies['Age'].replace({'13+': '13'}, inplace = True)
df_movies['Age'].replace({'16+': '16'}, inplace = True)
df_movies['Age'].replace({'18+': '18'}, inplace = True)
# df_movies['Age'] = df_movies['Age'].astype(int)
 
# IMDb
# df_movies.fillna({'IMDb' : df_movies['IMDb'].mean()}, inplace = True)
# df_movies.fillna({'IMDb' : df_movies['IMDb'].median()}, inplace = True)
df_movies.fillna({'IMDb' : "NA"}, inplace = True)
 
# Rotten Tomatoes
df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].astype(int)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].mean()}, inplace = True)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].median()}, inplace = True)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'].astype(int)
df_movies.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
 
# Directors
# df_movies = df_movies.drop(['Directors'], axis = 1)
df_movies.fillna({'Directors' : "NA"}, inplace = True)
 
# Cast
df_movies.fillna({'Cast' : "NA"}, inplace = True)
 
# Genres
df_movies.fillna({'Genres': "NA"}, inplace = True)
 
# Country
df_movies.fillna({'Country': "NA"}, inplace = True)
 
# Language
df_movies.fillna({'Language': "NA"}, inplace = True)
 
# Plotline
df_movies.fillna({'Plotline': "NA"}, inplace = True)
 
# Runtime
# df_movies.fillna({'Runtime' : df_movies['Runtime'].mean()}, inplace = True)
# df_movies['Runtime'] = df_movies['Runtime'].astype(int)
df_movies.fillna({'Runtime' : "NA"}, inplace = True)
 
# Kind
# df_movies.fillna({'Kind': "NA"}, inplace = True)
 
# Type
# df_movies.fillna({'Type': "NA"}, inplace = True)
# df_movies = df_movies.drop(['Type'], axis = 1)
 
# Seasons
# df_movies.fillna({'Seasons': 1}, inplace = True)
# df_movies.fillna({'Seasons': "NA"}, inplace = True)
df_movies = df_movies.drop(['Seasons'], axis = 1)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# df_movies.fillna({'Seasons' : df_movies['Seasons'].mean()}, inplace = True)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
 
# Service Provider
df_movies['Service Provider'] = df_movies.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_movies.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)

# Removing Duplicate and Missing Entries
df_movies.dropna(how = 'any', inplace = True)
df_movies.drop_duplicates(inplace = True)
In [10]:
data_investigate(df_movies)

No of Rows :  16923
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
       'Service Provider'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb                object
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime             object
Kind                object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Service Provider    object
dtype: object
**************************************************
Missing Values : 
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :

ID                  0.0
Title               0.0
Year                0.0
Age                 0.0
IMDb                0.0
Rotten Tomatoes     0.0
Directors           0.0
Cast                0.0
Genres              0.0
Country             0.0
Language            0.0
Plotline            0.0
Runtime             0.0
Kind                0.0
Netflix             0.0
Hulu                0.0
Prime Video         0.0
Disney+             0.0
Type                0.0
Service Provider    0.0
dtype: float64
**************************************************
Pictorial Representation :
In [11]:
df_movies.head()
Out[11]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country Language Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider
0 1 Inception 2010 13 8.8 87 Christopher Nolan Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French Dom Cobb is a skilled thief, the absolute best... 148 movie 1 0 0 0 0 Netflix
1 2 The Matrix 1999 16 8.7 88 Lana Wachowski,Lilly Wachowski Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... Action,Sci-Fi United States English Thomas A. Anderson is a man living two lives. ... 136 movie 1 0 0 0 0 Netflix
2 3 Avengers: Infinity War 2018 13 8.4 85 Anthony Russo,Joe Russo Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... Action,Adventure,Sci-Fi United States English As the Avengers and their allies have continue... 149 movie 1 0 0 0 0 Netflix
3 4 Back to the Future 1985 7 8.5 96 Robert Zemeckis Michael J. Fox,Christopher Lloyd,Lea Thompson,... Adventure,Comedy,Sci-Fi United States English Marty McFly, a typical American teenager of th... 116 movie 1 0 0 0 0 Netflix
4 5 The Good, the Bad and the Ugly 1966 16 8.8 97 Sergio Leone Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... Western Italy,Spain,West Germany,United States Italian Blondie (The Good) (Clint Eastwood) is a profe... 161 movie 1 0 1 0 0 Netflix
In [12]:
df_movies.describe()
Out[12]:
ID Year Netflix Hulu Prime Video Disney+ Type
count 16923.000000 16923.000000 16923.000000 16923.000000 16923.000000 16923.000000 16923.0
mean 8462.000000 2003.211901 0.214915 0.062637 0.727235 0.033150 0.0
std 4885.393638 20.526532 0.410775 0.242315 0.445394 0.179034 0.0
min 1.000000 1901.000000 0.000000 0.000000 0.000000 0.000000 0.0
25% 4231.500000 2001.000000 0.000000 0.000000 0.000000 0.000000 0.0
50% 8462.000000 2012.000000 0.000000 0.000000 1.000000 0.000000 0.0
75% 12692.500000 2016.000000 0.000000 0.000000 1.000000 0.000000 0.0
max 16923.000000 2020.000000 1.000000 1.000000 1.000000 1.000000 0.0
In [13]:
df_movies.corr()
Out[13]:
ID Year Netflix Hulu Prime Video Disney+ Type
ID 1.000000 -0.217816 -0.644470 -0.129926 0.469301 0.263530 NaN
Year -0.217816 1.000000 0.256151 0.101337 -0.255578 -0.047258 NaN
Netflix -0.644470 0.256151 1.000000 -0.118032 -0.745141 -0.089649 NaN
Hulu -0.129926 0.101337 -0.118032 1.000000 -0.284654 -0.039693 NaN
Prime Video 0.469301 -0.255578 -0.745141 -0.284654 1.000000 -0.289008 NaN
Disney+ 0.263530 -0.047258 -0.089649 -0.039693 -0.289008 1.000000 NaN
Type NaN NaN NaN NaN NaN NaN NaN
In [14]:
# df_movies.sort_values('Year', ascending = True)
# df_movies.sort_values('IMDb', ascending = False)
In [15]:
# df_movies.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_ottmovies.csv', index = False)
 
# path = '/content/drive/MyDrive/Files/'
 
# udf_movies = pd.read_csv(path + 'updated_ottmovies.csv')
 
# udf_movies
In [16]:
# df_netflix_movies = df_movies.loc[(df_movies['Netflix'] > 0)]
# df_hulu_movies = df_movies.loc[(df_movies['Hulu'] > 0)]
# df_prime_video_movies = df_movies.loc[(df_movies['Prime Video'] > 0)]
# df_disney_movies = df_movies.loc[(df_movies['Disney+'] > 0)]
In [17]:
df_netflix_only_movies = df_movies[(df_movies['Netflix'] == 1) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_hulu_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 1) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_prime_video_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 1 ) & (df_movies['Disney+'] == 0)]
df_disney_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 1)]
In [18]:
df_movies_plotline = df_movies.copy()
In [19]:
df_movies_plotline.drop(df_movies_plotline.loc[df_movies_plotline['Plotline'] == "NA"].index, inplace = True)
# df_movies_plotline = df_movies_plotline[df_movies_plotline.Plotline != "NA"]
In [20]:
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_plotline_movies = df_movies_plotline.loc[df_movies_plotline['Netflix'] == 1]
hulu_plotline_movies = df_movies_plotline.loc[df_movies_plotline['Hulu'] == 1]
prime_video_plotline_movies = df_movies_plotline.loc[df_movies_plotline['Prime Video'] == 1]
disney_plotline_movies = df_movies_plotline.loc[df_movies_plotline['Disney+'] == 1]
In [21]:
plt.figure(figsize = (10, 10))
corr = df_movies_plotline.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, allow annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()

<Figure size 720x720 with 0 Axes>
In [22]:
df_movies_plotline = df_movies_plotline['Plotline']
movies_plotline_w = ' '.join(df_movies_plotline)
In [23]:
stopwords = set(STOPWORDS)
 
wordcloud_all_plotline_movies = WordCloud(width = 1000, height = 500,
                background_color ='white',
                stopwords = stopwords,
                min_font_size = 10).generate(movies_plotline_w)
  
# plot the WordCloud image                       
plt.figure(figsize = (20, 10), facecolor = None)
plt.imshow(wordcloud_all_plotline_movies)
plt.axis("off")
plt.tight_layout(pad = 0)
  
plt.show()
In [24]:
movies_plotline_w = movies_plotline_w.lower()

stop_words_english_movies = set(STOPWORDS)

word_tokens_english_movies = word_tokenize(movies_plotline_w)

filtered_movie_plotline = [w for w in word_tokens_english_movies if not w in stop_words_english_movies]

filtered_movie_plotline = " ".join(filtered_movie_plotline)

filtered_movie_plotline = re.sub("'s", '', filtered_movie_plotline)

filtered_movie_plotline = re.sub(r'[0-9]+', '', filtered_movie_plotline)

final_movie_plotline = re.sub(r'[^\w\s]', '', filtered_movie_plotline)

plotline_movies_corpus_len = len(filtered_movie_plotline.split())
plotline_movies_corpus_len
Out[24]:
818331
In [25]:
def extract_ngrams(data, num):
    n_grams = ngrams(nltk.word_tokenize(data), num)
    return [ ' '.join(grams) for grams in n_grams]
In [26]:
plotline_ngram1_movies = FreqDist()

plotline_ngram1 = extract_ngrams(final_movie_plotline[:plotline_movies_corpus_len], 1)

for word in plotline_ngram1:
    plotline_ngram1_movies[word.lower()] += 1
In [27]:
plotline_ngram1_movies.most_common(10)
Out[27]:
[('life', 627),
 ('one', 624),
 ('will', 577),
 ('family', 437),
 ('new', 414),
 ('love', 408),
 ('world', 402),
 ('story', 365),
 ('two', 363),
 ('man', 351)]
In [28]:
plotline_ngram2_movies = FreqDist()

plotline_ngram2 = extract_ngrams(final_movie_plotline[:plotline_movies_corpus_len], 2)

for word in plotline_ngram2:
    plotline_ngram2_movies[word.lower()] += 1
In [29]:
plotline_ngram2_movies.most_common(10)
Out[29]:
[('high school', 69),
 ('new york', 61),
 ('one day', 57),
 ('best friend', 54),
 ('young man', 43),
 ('falls love', 42),
 ('ca nt', 40),
 ('years later', 39),
 ('young woman', 39),
 ('year old', 34)]
In [30]:
plotline_ngram3_movies = FreqDist()

plotline_ngram3 = extract_ngrams(final_movie_plotline[:plotline_movies_corpus_len], 3)

for word in plotline_ngram3:
    plotline_ngram3_movies[word.lower()] += 1
In [31]:
plotline_ngram3_movies.most_common(10)
Out[31]:
[('new york city', 29),
 ('based true story', 11),
 ('story revolves around', 8),
 ('world war ii', 7),
 ('game cat mouse', 7),
 ('will stop nothing', 6),
 ('academy award nominated', 6),
 ('high school senior', 6),
 ('year high school', 6),
 ('make matters worse', 6)]
In [32]:
plotline_ngram4_movies = FreqDist()

plotline_ngram4 = extract_ngrams(final_movie_plotline[:plotline_movies_corpus_len], 4)

for word in plotline_ngram4:
    plotline_ngram4_movies[word.lower()] += 1
In [33]:
plotline_ngram4_movies.most_common(10)
Out[33]:
[('barcelona catalonia northeast spain', 5),
 ('academy award nominated filmmaker', 3),
 ('james bond pierce brosnan', 3),
 ('dame kristin scott thomas', 3),
 ('will change life forever', 3),
 ('deadly game cat mouse', 3),
 ('basque country north spain', 3),
 ('make world better place', 2),
 ('will whatever takes achieve', 2),
 ('agent james bond pierce', 2)]
In [34]:
plotline_ngram5_movies = FreqDist()

plotline_ngram5 = extract_ngrams(final_movie_plotline[:plotline_movies_corpus_len], 5)

for word in plotline_ngram5:
    plotline_ngram5_movies[word.lower()] += 1
In [35]:
plotline_ngram5_movies.most_common(10)
Out[35]:
[('agent james bond pierce brosnan', 2),
 ('emerging demand hyperfuel resources han', 2),
 ('demand hyperfuel resources han solo', 2),
 ('hyperfuel resources han solo finds', 2),
 ('resources han solo finds middle', 2),
 ('han solo finds middle heist', 2),
 ('solo finds middle heist alongside', 2),
 ('finds middle heist alongside criminals', 2),
 ('middle heist alongside criminals meet', 2),
 ('heist alongside criminals meet likes', 2)]
In [36]:
# Netflix Wordcloud
netflix_plotline_movies_t = netflix_plotline_movies['Plotline']
netflix_movies_plotline_w = ' '.join(netflix_plotline_movies_t)
In [37]:
stopwords = set(STOPWORDS)
 
wordcloud_netflix_plotline_movies = WordCloud(width = 1000, height = 500,
                                           background_color ='white',
                                           stopwords = stopwords,
                                           min_font_size = 10
                                          ).generate(netflix_movies_plotline_w)

print('\nThe Wordcloud Generated from Plotlines of Netflix is : \n')
# plot the WordCloud image                       
plt.figure(figsize = (20, 10), facecolor = None)
plt.imshow(wordcloud_netflix_plotline_movies)
plt.axis("off")
plt.tight_layout(pad = 0)
  
plt.show()

The Wordcloud Generated from Plotlines of Netflix is :
In [38]:
# Hulu Wordcloud
hulu_plotline_movies_t = hulu_plotline_movies['Plotline']
hulu_movies_plotline_w = ' '.join(hulu_plotline_movies_t)
In [39]:
stopwords = set(STOPWORDS)
 
wordcloud_hulu_plotline_movies = WordCloud(width = 1000, height = 500,
                                        background_color ='white',
                                        stopwords = stopwords,
                                        min_font_size = 10
                                       ).generate(hulu_movies_plotline_w)
  
print('\nThe Wordcloud Generated from Plotlines of Hulu is : \n')
# plot the WordCloud image                       
plt.figure(figsize = (20, 10), facecolor = None)
plt.imshow(wordcloud_hulu_plotline_movies)
plt.axis("off")
plt.tight_layout(pad = 0)
  
plt.show()

The Wordcloud Generated from Plotlines of Hulu is :
In [40]:
# Prime Video Wordcloud
prime_video_plotline_movies_t = prime_video_plotline_movies['Plotline']
prime_video_movies_plotline_w = ' '.join(prime_video_plotline_movies_t)
In [41]:
stopwords = set(STOPWORDS)
 
wordcloud_prime_video_plotline_movies = WordCloud(width = 1000, height = 500,
                                               background_color ='white',
                                               stopwords = stopwords,
                                               min_font_size = 10
                                              ).generate(prime_video_movies_plotline_w)
  
print('\nThe Wordcloud Generated from Plotlines of Prime Video is : \n')
# plot the WordCloud image                       
plt.figure(figsize = (20, 10), facecolor = None)
plt.imshow(wordcloud_prime_video_plotline_movies)
plt.axis("off")
plt.tight_layout(pad = 0)
  
plt.show()

The Wordcloud Generated from Plotlines of Prime Video is :
In [42]:
# Disney+ Wordcloud
disney_plotline_movies_t = disney_plotline_movies['Plotline']
disney_movies_plotline_w = ' '.join(disney_plotline_movies_t)
In [43]:
stopwords = set(STOPWORDS)
 
wordcloud_disney_plotline_movies = WordCloud(width = 1000, height = 500,
                                          background_color ='white',
                                          stopwords = stopwords,
                                          min_font_size = 10
                                         ).generate(disney_movies_plotline_w)
  
print('\nThe Wordcloud Generated from Plotlines of Disney+ is : \n')
# plot the WordCloud image                       
plt.figure(figsize = (20, 10), facecolor = None)
plt.imshow(wordcloud_disney_plotline_movies)
plt.axis("off")
plt.tight_layout(pad = 0)
  
plt.show()

The Wordcloud Generated from Plotlines of Disney+ is :